R: Weighted Empirical Adaptive Variance Estimation

weightsLumley {sandwich} | R Documentation

## Weighted Empirical Adaptive Variance Estimation

### Description

A set of functions implementing a class of kernel-based heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimators as introduced by Andrews (1991).

### Usage

    weave(x, order.by = NULL, prewhite = FALSE, C = NULL,
      method = c("truncate", "smooth"), acf = isoacf, adjust = FALSE,
      diagnostics = FALSE, sandwich = TRUE, tol = 1e-7, data = list(), ...)

    weightsLumley(x, order.by = NULL, C = NULL,
      method = c("truncate", "smooth"), acf = isoacf, tol = 1e-7, data = list(), ...)

### Arguments

`x` | a fitted model object.
`order.by` | Either a vector `z` or a formula with a single explanatory variable like `~ z`. The observations in the model are ordered by the size of `z`. If set to `NULL` (the default) the observations are assumed to be ordered (e.g., a time series).
`prewhite` | logical or integer. Should the estimating functions be prewhitened? If `TRUE` or greater than 0 a VAR model of order `as.integer(prewhite)` is fitted via `ar` with method `"ols"` and `demean = FALSE`.
`C` | numeric. The cutoff constant `C` is by default 4 for method `"truncate"` and 1 for method `"smooth"`.
`method` | a character specifying the method used, see details.
`acf` | a function that computes the autocorrelation function of a vector, by default `isoacf` is used.
`adjust` | logical. Should a finite sample adjustment be made? This amounts to multiplication with n/(n-k) where n is the number of observations and k the number of estimated parameters.
`diagnostics` | logical. Should additional model diagnostics be returned? See `vcovHAC` for details.
`sandwich` | logical. Should the sandwich estimator be computed? If set to `FALSE` only the middle matrix is returned.
`tol` | numeric. Weights that exceed `tol` are used for computing the covariance matrix, all other weights are treated as 0.
`data` | an optional data frame containing the variables in the `order.by` model. By default the variables are taken from the environment which the function is called from.
`...` | currently not used.

### Details

`weave` is a convenience interface to `vcovHAC` using `weightsLumley`: first a weights function is defined and then `vcovHAC` is called.

Both weighting methods are based on some estimate of the autocorrelation function r (as computed by `acf`) of the residuals of the model `x`. The weights for the `"truncate"` method are

I{n * r ** 2 > C}

and the weights for the `"smooth"` method are

min{1, C * n * r ** 2}

where n is the number of observations in the model an C is the truncation constant `C`.

Further details can be found in Lumley & Heagerty (1999).

### Value

`weave` returns the same type of object as `vcovHAC` which is typically just the covariance matrix.

`weightsLumley` returns a vector of weights.

### References

Lumley T & Heagerty P (1999), Weighted Empirical Adaptive Variance Estimators for Correlated Data Regression. Journal of the Royal Statistical Society B, 61, 459–477.

### See Also

`vcovHAC`, `weightsAndrews`, `kernHAC`

### Examples

    x <- sin(1:100)
    y <- 1 + x + rnorm(100)
    fm <- lm(y ~ x)
    weave(fm)
    vcov(fm)

[Package sandwich version 2.3-4 Index]